## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Block_num 19558 9778.9 2 50 94.469 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Block_num 56197 28099 2 40 21.669 4.211e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Block_num 72764 36382 2 90 57.3998 < 2.2e-16 ***
## Age 21165 21165 1 45 33.3916 6.649e-07 ***
## Block_num:Age 6888 3444 2 90 5.4339 0.005917 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Block_num 328941 164471 2 50 47.81 2.476e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Block_num 486099 243050 2 40 12.096 7.793e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
corr_ct_ya <- cor.test(data_ya_mean$Total_Time, data_ya_mean$NARA)
corr_ct_ya
##
## Pearson's product-moment correlation
##
## data: data_ya_mean$Total_Time and data_ya_mean$NARA
## t = -2.1853, df = 24, p-value = 0.03886
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.68641361 -0.02377631
## sample estimates:
## cor
## -0.4073764
corr_ct_oa <- cor.test(data_oa_mean$Total_Time, data_oa_mean$NARA)
corr_ct_oa
##
## Pearson's product-moment correlation
##
## data: data_oa_mean$Total_Time and data_oa_mean$NARA
## t = -4.075, df = 19, p-value = 0.0006456
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.8608260 -0.3562548
## sample estimates:
## cor
## -0.6829156
corr_dt_ya <- cor.test(data_ya_mean$Distance, data_ya_mean$NARA)
corr_dt_ya
##
## Pearson's product-moment correlation
##
## data: data_ya_mean$Distance and data_ya_mean$NARA
## t = -3.1643, df = 24, p-value = 0.004187
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7684277 -0.1965251
## sample estimates:
## cor
## -0.542574
corr_dt_oa <- cor.test(data_oa_mean$Distance, data_oa_mean$NARA)
corr_dt_oa
##
## Pearson's product-moment correlation
##
## data: data_oa_mean$Distance and data_oa_mean$NARA
## t = -4.0828, df = 19, p-value = 0.0006342
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.8611634 -0.3573930
## sample estimates:
## cor
## -0.683611
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 5998 5998 10.06 0.00273 **
## Residuals 45 26833 596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1